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While 360° panoramic virtual tours are widely used for remote spatial exploration, there is no standardized method for determining the placement or sequencing of such viewpoints. Computational solutions like the Art Gallery Problem can determine minimal viewpoints that ensure the building’s coverage, however cognitive research has not addressed whether these are sufficient to support the viewer’s understanding of the space. In other words, would the hypothetical guards understand their gallery’s layout? Drawing on the Network of Reference Frames theory and prior research on spatial integration mechanisms and associated integration costs, this study investigates how three design factors - visual overlap, route-based sequencing, and landmark availability - influence layout understanding from a minimal set of views. A reproducible web-based experimental framework was developed to enable systematic manipulation of viewpoint configurations and detailed behavioral logging. Fifty participants explored five systematically varied configurations across five building layouts and were tasked with recognizing the correct layout among visually similar alternatives, reporting confidence after each decision. Monetary incentives were used to encourage attentive exploration and constrain strategy use. Results revealed that while individual spatial ability is a strong predictor of layout understanding (p < .001), viewpoint design significantly modulates that understanding. Specifically, visual overlap between consecutive views significantly enhanced layout understanding only when paired with additional cues - namely landmarks or route-based sequencing. Among these, landmarks significantly improved layout recognition (p < .05), unlike route-based sequencing. Moreover, combining both cues yielded no additional benefit. These findings demonstrate how viewpoint configurations can be designed to better support layout understanding from a minimal set of views. In addition, the proposed replicable, web-based experimental pipeline enables systematic evaluation and optimization of viewpoint designs used in spatial learning and navigation tools.